Data Collection

Google Lens has revolutionized how we interact with visual content – it allows users to search the web using images rather than text queries. This powerful visual search engine can identify objects, text, landmarks, products, and much more from uploaded images. In this guide, we'll explore the types of data that can be scraped from Google Lens, examine various methods for extracting this information, and demonstrate how to efficiently collect visual search results using our Web Scraping API.

Getting good data at scale is crucial when you're running AI-powered business operations. Sure, AI tools can help with data collection, but they're definitely not all created equal. We'll walk through the best AI data collection platforms out there, break down what works and what doesn't, and help you figure out which one makes sense for what you're trying to do, whether you're putting together a machine learning pipeline or just trying to automate all that tedious data entry work.

You don't need to be Google or work at a university to train your own AI model anymore. Small teams can build smart systems that actually work for what they need - you just need the right tools and know-how. This guide walks you through everything from figuring out what problem you're trying to solve all the way to getting your model up and running and keeping it working.

Ever wondered how some app developers always seem one step ahead on Google Play? The secret often comes down to data – lots of it. Instead of waiting around for monthly “Top Charts” updates, the smartest teams use Google Play scrapers to track real-time metrics and stay ahead of the competition. In this article, you’ll learn how to do exactly that, gaining the tools to effortlessly scrape everything from download totals to one-star rant emojis.

Target is one of the largest retailers in the US, offering a wide range of products, from electronics to groceries. Scraping product data can help you track prices, monitor trends, or build comparison tools to enhance your purchasing decisions. This guide outlines the process, provides suggestions, and provides instructions on how to extract data, such as prices and ratings, efficiently.

Walmart’s digital marketplace is a vast platform, featuring over 420 million products and nearly 500 million monthly visits. That volume of web data is a valuable source for eCommerce teams, data analysts, and investment firms seeking pricing intelligence, inventory trends, and competitive insights. But scraping it isn’t easy – Walmart uses a complex, multi-layer anti-bot system that stops most common scraping tools. In this guide, you’ll learn the proven methods that work in 2026.

When it comes to gathering online data, two terms often create confusion: web crawling and web scraping. Although both involve extracting information from websites, they serve different purposes and employ distinct methods. In this article, we’ll break down these concepts, show you how they work, and help you decide which one suits your data extraction needs.

Google Finance is one of the most comprehensive financial data platforms, offering real-time stock prices, market analytics, and company insights. Scraping Google Finance provides access to valuable data streams that can transform your analysis capabilities. In this guide, we'll walk through building a robust Google Finance scraper using Python, handling anti-bot measures, and implementing best practices for reliable data extraction.

YouTube handles over 3B searches every month, making it the world’s second-largest search engine. Tapping into that web data uncovers trending topics, competitor strategies, and content gaps you can exploit. However, extracting that information requires navigating YouTube’s sophisticated CAPTCHAs and technical hurdles. In this guide, you’ll learn some proven approaches on how to scrape YouTube search results at scale and choose the right method for your specific needs.

Zillow hosts millions of real estate listings across the U.S., but manually collecting that data is slow and error-prone. This guide walks you through how to scrape Zillow data effectively and ethically. You’ll learn what kind of data is accessible, which tools to use, and how to handle anti-scraping challenges to keep your pipeline running smoothly.

Your company's data is everywhere – databases, spreadsheets, legacy systems, but most sits unused because nobody knows how to connect the pieces. Businesses waste millions rebuilding what already exists. Data mapping fixes this chaos. It bridges isolated information and creates actionable insights. AI now makes it automatic, and today we're exploring how it does so.

ZoomInfo is a goldmine for B2B teams – over 100M company profiles and 260M contacts, all in one place. But getting that data isn’t easy. With strict defenses like CAPTCHAs, browser fingerprinting, and aggressive IP bans, most scrapers fail after just a few requests. That’s where this guide comes in. We’ll show you how to bypass ZoomInfo’s countermeasures and extract clean, actionable data at scale.

After years of progress, AI has gotten a lot better at acting like human thinking. Whether that’s in machine learning, robotics, natural language processing (NLP), or training AI agents. But one thing still holds true – AI is only as good as the data it learns from. In this post, we’ll look at why high-quality training data matters so much when building strong AI systems.

Google Shopping is a product search engine that aggregates listings from thousands of online retailers. Businesses scrape it to track competitor pricing, spot trends, and gather valuable eCommerce insights. Using APIs, no-code tools, or custom scripts, you can extract data like product titles, prices, ratings, and more. In this guide, we’ll build a custom scraping script using Python and Playwright!

Web scraping is the art of extracting data from websites, and it's become a go-to tool for developers, data analysts, and startup teams. While Google gets most of the spotlight, scraping Bing search results can be a smart move, especially for regional insights or less saturated SERPs. In this guide, we'll show you how to scrape Bing using Python with tools like Requests, Beautiful Soup, and Playwright.

Google Scholar is a free search engine for academic articles, books, and research papers. If you're gathering academic data for research, analysis, or application development, this blog post will give you a reliable foundation. In this guide, you'll learn how to scrape Google Scholar with Python, set up proxies to avoid IP bans, build a working scraper, and explore advanced tips for scaling your data collection.

Scraping Amazon without proxies leads to IP bans, CAPTCHAs, and rate limits, making data collection nearly impossible. Proxies are essential for bypassing these defenses and accessing vital pricing and product data. This guide explains why scraping Amazon is challenging, how proxies can help, and which types of proxies are most effective for reliable, large-scale Amazon data extraction.

Web scraping should be simple. Yet, choosing the right solution often feels like a challenge – different APIs for different targets, multiple subscriptions, and unnecessary complexity. That’s why we’re introducing a more convenient way to collect data from various targets – our four scraping APIs are becoming one, more powerful than ever, Web Scraping API. Now, you can collect data from all targets – eCommerce, SERPs, social media, and web, with one unified API.